There is no explicit attention mechanism in CoCNN unlike self attention based Transformers [1]}, [2]} or weighted attention based LSTMs [3]}, [4]}.
Attention mechanism is important for obtaining the importance of each input word in terms of output prediction. Figure REF  demonstrates an over simplified notion of how 1D conv implicitly imposes attention on a sequence containing 7 entities \(S_1, S_2, \ldots S_7\) . After employing a 1D conv filter of size 3 on this sequence representation, we obtain a vector containing values \(O_1, O_2, \ldots O_5\) , where we assume \(O_5\)  and \(O_1\)  to be the maximum for the left and the right figure, respectively. \(O_5\)  (left figure) is obtained by convolving input entity \(S_5\) , \(S_6\)  and \(S_7\)  situated at the end of input sequence. We can say that these three input entities have been paid more attention to by our conv filter than the other 4 entities. In the right figure, \(S_5\) , \(S_6\)  and \(S_7\)  are situated at the beginning of the input sequence. Our conv filter gives similar importance to this pattern irrespective of its position change. Such positional independence of important patterns helps in Bengali and Hindi LM where input words are loosely bound and words themselves are highly inflected. In CoCNN, such attention is imposed on characters of each word during CNNvec representation generation of that word using VL, while similar type of attention is imposed on words of our input sentence/ paragraph while obtaining Coordination vector using TC.
